OOD Generalization

OOD: Out-of-Distribution

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29 papers in the last four weeks, up 263% on the four weeks before. 0.3% of all new papers.

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Efficient Prompt Learning for Traffic Forecasting
May 8, 2026Qianru Zhang, Xinyi Gao, Alexander Zhou +3OOD Generalization Prompt Tuning
School of Computing and Data Science The University of Hong Kong · School of Electrical Engineering and Computer Science The University of Queensland · Department of Computing The Hong Kong Polytechnic University
On the Safety of Graph Representation Learning
May 7, 2026Xiaoguang Guo, Zehong Wang, Ziming Li +5Graph Representation Learning Algorithmic Fairness
University of Connecticut · University of Notre Dame
Agentic AIs Are the Missing Paradigm for Out-of-Distribution Generalization in Foundation Models
May 7, 2026Xin Wang, Haibo Chen, Wenxuan Liu +1OOD Generalization Agentic AI
Tsinghua University
Learning beyond Site Bias for OOD Generalization in Brain Networks
May 7, 2026Yingxu Wang, Kunyu Zhang, Yanwu Yang +5Functional Connectivity Domain Generalization
Zhengzhou University · City University of Hong Kong
EGA: Adapting Frozen Encoders for Vector Search with Bounded Out-of-Distribution Degradation
May 7, 2026Dongfang ZhaoOOD Generalization Vision Foundation Model Adaptation
Tacoma School of Engineering and Technology University of Washington
CPCANet: Deep Unfolding Common Principal Component Analysis for Domain Generalization
May 6, 2026Yu-Hsi Chen, Abd-Krim SeghouanePrincipal Component Analysis Domain Generalization
The University of Melbourne
Replay-Based Continual Learning for Physics-Informed Neural Operators
May 6, 2026Yizheng Wang, Mohammad Sadegh Eshaghi, Xiaoying Zhuang +2OOD Generalization Replay-Based Continual Learning
Department of Engineering Mechanics, Tsinghua University, Beijing 100084, China · Institute of Photonics, Department of Mathematics and Physics, Leibniz University Hannover, Germany · Institute of Structural Mechanics, Bauhaus-Universität Weimar, Marienstr. 15, D-99423 Weimar, Germany
Critical Windows of Complexity Control: When Transformers Decide to Reason or Memorize
May 6, 2026Sarwan AliDeep Learning Optimization Weight Decay
Columbia University, Irving Medical Center, NY, USA
Perturbation is All You Need for Extrapolating Language Models
May 5, 2026Zetai Cen, Jin Zhu, Xinwei Shen +1OOD Generalization Autoregressive Language Modeling
School of Mathematics, University of Bristol · School of Mathematics, University of Birmingham · Department of Statistics, University of Washington +1
Task Vector Geometry Underlies Dual Modes of Task Inference in Transformers
May 5, 2026Hao Yan, Haolin Yang, Yiqiao ZhongTransformer Interpretability Neural Representation Geometry
University of Wisconsin–Madison · University of Chicago
RFPrompt: Prompt-Based Expert Adaptation of the Large Wireless Model for Modulation Classification
May 5, 2026Md Raihan Uddin, Tolunay Seyfi, Fatemeh AfghahWireless Foundation Models Automatic Modulation Classification
Department of Electrical and Computer Engineering, Clemson University, Clemson, SC, USA
Extrapolation in Statistical Learning with Extreme Value Theory
May 3, 2026Sebastian Engelke, Nicola Gnecco, Anne SabourinOOD Generalization Statistical Learning Theory
Research Institute for Statistics and Information Science, University of Geneva, Switzerland · Department of Mathematics, Imperial College London, United Kingdom · Université Paris Cité, Université Paris-Saclay, ENS Paris-Saclay, CNRS, SSA, INSERM, Centre Borelli, France
Omni-Fake: Benchmarking Unified Multimodal Social Media Deepfake Detection
May 2, 2026Tianxiao Li, Zhenglin Huang, Haiquan Wen +10Explainable Deepfake Detection OOD Generalization
University of Liverpool · University of Exeter · The Chinese University of Hong Kong, Shenzhen +1
Perturb and Correct: Post-Hoc Ensembles using Affine Redundancy
May 2, 2026Eleanor QuintDistribution Shift Robustness Ensemble Learning
Department of Computing University of Nebraska–Lincoln Lincoln, NE 68508, USA
Beyond the Training Distribution: Mapping Generalization Boundaries in Neural Program Synthesis
Apr 30, 2026Henrik Voigt, Michael Habeck, Joachim GiesenOOD Generalization LLM-Based Program Synthesis
Friedrich Schiller University · Jena, Germany
Detecting is Easy, Adapting is Hard: Local Expert Growth for Visual Model-Based Reinforcement Learning under Distribution Shift
Apr 30, 2026Haiyang ZhaoDistribution Shift Robustness OOD Generalization
University of Georgia United States
Diffusion Reconstruction towards Generalizable Audio Deepfake Detection
Apr 29, 2026Bo Cheng, Songjun Cao, Xiaoming Zhang +3Audio Deepfake Detection OOD Generalization
Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China · Tencent Youtu Lab, China
SIEVES: Selective Prediction Generalizes through Visual Evidence Scoring
Apr 28, 2026Hector G. Rodriguez, Marcus RohrbachVisual Question Answering VLM Robustness
TU Darmstadt & hessian.AI, Germany
Geometry-free prediction of inertial lift forces in microfluidic devices using deep learning
Apr 27, 2026Jesse Ward-Bond, Ali Mashadian, Timothy C. Y. Chan +1Neural Surrogate Modeling OOD Generalization
aDepartment of Mechanical & Industrial Engineering, University of Toronto · bMechanical Engineering, Purdue University · cDepartment of Materials Science & Engineering, University of Toronto +1
Useful nonrobust features are ubiquitous in biomedical images
Apr 24, 2026Coenraad Mouton, Randle Rabe, Niklas C. Koser +4Healthcare Neural Network Robustness
Section Biomedical Imaging, Department of Radiology and Neuroradiology, University Hospital Schleswig-Holstein, Kiel University · Faculty of Engineering, North-West University, South Africa
Robust Out-of-Distribution Stochastic Optimization
Apr 22, 2026Xianyu Li, Huan Xu, Xiaolin Huang +1Stochastic Optimization OOD Generalization
Department of Automation, Tsinghua University · Antai College of Economics & Management, Shanghai Jiao Tong University · Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University
Learning Posterior Predictive Distributions for Node Classification from Synthetic Graph Priors
Apr 21, 2026Jeongwhan Choi, Jongwoo Kim, Woosung Kang +1Synthetic Data Pretraining OOD Generalization
KAIST Daejeon, Republic of Korea
Superficial Success vs. Internal Breakdown: An Empirical Study of Generalization in Adaptive Multi-Agent Systems
Apr 21, 2026Namyoung So, Seokgyu Jang, Taeuk KimMulti-Agent Coordination OOD Generalization
Department of Computer Science, Hanyang University, Seoul, Republic of Korea
TimeRFT: Stimulating Generalizable Time Series Forecasting for TSFMs via Reinforcement Finetuning
Apr 18, 2026Siyang Li, Yize Chen, Zijie Zhu +4Time Series Forecasting OOD Generalization
HKUST(GZ) Guangzhou, China · University of Alberta Edmonton, Canada · Alibaba Cloud Guangzhou, China +3
Late Fusion Neural Operators for Extrapolation Across Parameter Space in Partial Differential Equations
Apr 17, 2026Eva van Tegelen, Taniya Kapoor, George A. K. van Voorn +2Surrogate Modeling PDE Surrogate Modeling
Artificial Intelligence Group, Wageningen University and Research, Netherlands · Mathematical and Statistical Methods, Wageningen University and Research, Netherlands
Autonomous Vehicle Collision Avoidance With Racing Parameterized Deep Reinforcement Learning
Apr 17, 2026Shathushan Sivashangaran, Vihaan Dutta, Apoorva Khairnar +2OOD Generalization Autonomous Driving
Virginia Commonwealth University, Richmond, VA 23284, USA · University of Michigan, Ann Arbor, MI 48109, USA · Virginia Tech, Blacksburg, VA 24061, USA
Spotlights and Blindspots: Evaluating Machine-Generated Text Detection
Apr 17, 2026Kevin Stowe, Kailash PatilAI-Generated Text Detection Language Model Generation Evaluation
Pindrop
OT on the Map: Quantifying Domain Shifts in Geographic Space
Apr 17, 2026Haoran Zhang, Livia Betti, Konstantin Klemmer +2Distribution Shift Geospatial Representation Learning
Harvard University · University of Colorado Boulder · LGND AI, Inc. +3
Exploration of Fast-Slow Latent Recurrence for Train-Short, Test-Long Generalization
Apr 2, 2026Shota Takashiro, Masanori Koyama, Takeru Miyato +3OOD Generalization Recurrent Neural Networks
The University of Tokyo, Tokyo, Japan · University of Tübingen, Tübingen, Germany
Who Wrote the Book? Detecting and Attributing LLM Ghostwriters
Mar 30, 2026Anudeex Shetty, Qiongkai Xu, Olga Ohrimenko +1OOD Generalization Language Model Fingerprinting
School of Computing and Information Systems, The University of Melbourne, Australia · School of Computing, FSE, Macquarie University, Australia

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Efficient Prompt Learning for Traffic Forecasting

Qianru Zhang, Xinyi Gao, Alexander Zhou, Reynold Cheng+2

School of Computing and Data Science The University of Hong Kong · School of Electrical Engineering and Computer Science The University of Queensland · Department of Computing The Hong Kong Polytechnic University

OOD Generalization Prompt Tuning Spatiotemporal Forecasting Traffic Forecasting Spatiotemporal GNNs

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Accurate traffic prediction is essential for optimizing transportation systems, enhancing resource allocation, and improving overall urban administration. Spatio-temporal graph neural networks (GNNs) have achieved state-of-the-art performance and have been widely used in various spatio-temporal prediction scenarios. However, these prediction methods often exhibit low generalization ability, struggling with distribution shifts caused by spatio-temporal dynamics. To address this challenge, we propose an approach to enhance the generalization and adaptation of spatio-temporal GNNs through efficient prompting. Specifically, we introduce a lightweight and model-agnostic prompt tuning framework for spatio-temporal GNNs, named SimpleST. It facilitates adapting pre-trained spatio-temporal GNNs to novel distributions while keeping the model parameters fixed. This prompt mechanism reduces the overhead and complexity of adaptation, enabling efficient utilization of pre-trained models for out-of-distribution generalization. Extensive experiments conducted on five real-world urban spatio-temporal datasets demonstrate the superiority of our approach in terms of prediction accuracy and computational efficiency.

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